{"id":"W4414798945","doi":"10.1109/tim.2025.3617406","title":"Single-Beat Myocardial Infarction Detection and Localization Using PSO-Optimized Extra Trees on 15-Lead ECG","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Electrocardiography; Pattern recognition (psychology); Particle filter; Myocardial infarction; Reliability (semiconductor); Filter (signal processing)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003655618,0.0004922086,0.0007284526,0.0004321808,0.0002094747,0.0003767388,0.0003392921,0.000602826,0.0005709695],"category_scores_gemma":[0.001110541,0.000201608,0.0005166437,0.0003160904,0.0001566164,0.0002964239,0.0002624517,0.0003878492,0.0001782497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002211537,"about_ca_system_score_gemma":0.0003880899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006603033,"about_ca_topic_score_gemma":0.005694654,"domain_scores_codex":[0.9998566,0.00003857636,0.000008566685,0.00003627731,0.00003205345,0.00002794077],"domain_scores_gemma":[0.9997322,0.0001478244,0.00003084516,0.00001457397,0.00005585821,0.00001863823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001783287,0.0001231298,0.004318329,0.0000586039,0.00007340951,0.0002147784,0.00008008585,0.8035439,0.008457566,0.0008391012,0.001170544,0.1809422],"study_design_scores_gemma":[0.000002110759,0.00001362083,0.0003950112,0.000001319968,0.000002540168,0.000006380612,0.000002643,0.9992655,0.0002095962,0.00006714561,0.00003268449,0.000001469828],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4004813,0.0007044399,0.5952903,0.0002318732,0.00008978246,0.00005269132,0.0000940487,0.0008604987,0.002194956],"genre_scores_gemma":[0.9192834,0.0001895593,0.07890932,0.00006189872,0.0000237238,0.00003903032,0.0001572132,0.00002295349,0.001312979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006603033,"threshold_uncertainty_score":0.01312917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04668274555650907,"score_gpt":0.2833521912540732,"score_spread":0.2366694456975641,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}